# A4FN: an Agentic AI Architecture for Autonomous Flying Networks

arXiv: https://arxiv.org/abs/2510.03829 · HTML: https://mlsanity.com/paper/2510.03829/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 8 of its 18 references that are in the map.
- First posted on arXiv 4 Oct 2025.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 8 of 8)

- [ChatGPT for Robotics: Design Principles and Model Abilities](https://mlsanity.com/paper/2306.17582/) (2023)
- [Large Language Models for UAVs: Current State and Pathways to the Future](https://mlsanity.com/paper/2405.01745/) (2024)
- [Generative AI for Advanced UAV Networking](https://mlsanity.com/paper/2404.10556/) (2024)
- [Large Language Model Based Multi-Objective Optimization for Integrated Sensing and Communications in UAV Networks](https://mlsanity.com/paper/2410.05062/) (2024)
- [Large Language Model (LLM)-enabled Graphs in Dynamic Networking](https://mlsanity.com/paper/2407.20840/) (2024)
- [Intent-driven autonomous network and service management in future cellular networks: A structured literature review](https://mlsanity.com/paper/2108.04560/) (2021)
- [SUPPLY: Sustainable multi-UAV Performance-aware Placement Algorithm for Flying Networks](https://mlsanity.com/paper/2404.06197/) (2024)
- [A Framework Leveraging Large Language Models for Autonomous UAV Control in Flying Networks](https://mlsanity.com/paper/2506.04404/) (2025)

## Built on by (most cited first, 2 of 2)

- [Voice-Driven Semantic Perception for UAV-Assisted Emergency Networks](https://mlsanity.com/paper/2602.17394/) (2026)
- [When Agentic AI Meets Integrated Sensing and Communication](https://mlsanity.com/paper/2608.05792/) (2026)

## Abstract (arXiv)

This position paper presents A4FN, an Agentic Artificial Intelligence (AI) architecture for intent-driven automation in Flying Networks (FNs) using Unmanned Aerial Vehicles (UAVs) as access nodes. A4FN leverages Generative AI and Large Language Models (LLMs) to enable real-time, context-aware network control via a distributed agentic system. It comprises two components: the Perception Agent (PA), which semantically interprets multimodal input -- including imagery, audio, and telemetry data -- from UAV-mounted sensors to derive Service Level Specifications (SLSs); and the Decision-and-Action Agent (DAA), which reconfigures the network based on inferred intents. A4FN embodies key properties of Agentic AI, including autonomy, goal-driven reasoning, and continuous perception-action cycles. Designed for mission-critical, infrastructure-limited scenarios such as disaster response, it supports adaptive reconfiguration, dynamic resource management, and interoperability with emerging wireless technologies. The paper details the A4FN architecture, its core innovations, and open research challenges in multi-agent coordination and Agentic AI integration in next-generation FNs.